ConvOS:一种具有可解释性的新冠肺炎X光影像诊断模型

doi:10.3969/j.issn.1000-1158.2023.08.23

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Abstract In order to improve the diagnostic accuracy of deep learning model in X-ray diagnosis of COVID-19 and improve the models interpretability, a novel visual diagnosis (ConvOS) model was designed by combining bilateral perturbation comprehensive Gradient significance graph optimization (IGOS++) algorithm and improved ConvNeXt network model. An efficient channel attention (ECA) module was introduced into the residual block of ConvNeXt network model, and IGOS++ was used to analyze the output characteristics of the improved ConvNeXt network model. The optimal insertion loss hyperparameters were obtained, and high-precision saliency maps were generated to improve the performance of visual interpretation and diagnosis of COVID-19 X-ray images. The experimental results on COVIDx dataset show that the accuracy, recall, accuracy and F1-score of ConvOS model reach 93.7%, 92.6%, 96.2% and 94.4%, respectively. Compared with other diagnostic models, the classification performance index is better, the region of interest (ROI) of significance graph is more accurate, and the diagnostic confidence of the model is higher.

Key words： metrology COVID-19 X-ray radiography ConvOS model IGOS++ algorithm ConvNeXt network interpretability deep learning model

Received: 21 July 2022 Published: 22 August 2023

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	Articles by authors
	JIAN Xian-zhong
	YOU Guo-da
	ZHANG Tao
	ZHANG Zhen-wen

Cite this article:

JIAN Xian-zhong,YOU Guo-da,ZHANG Tao, et al. ConvOS:An Interpretable Diagnostic Model for COVID-19 X-ray Imaging[J]. Acta Metrologica Sinica, 2023, 44(8): 1303-1309.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2023.08.23 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2023/V44/I8/1303

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